Using hybrid machine learning model with an improved driving training-based optimization algorithm for wind power forecasting

R. J. Kuo

doi:10.1007/s00521-026-12401-8

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Using hybrid machine learning model with an improved driving training-based optimization algorithm for wind power forecasting

2026-09-08

R. J. Kuo *

Yun-Tung Ko

DOI：10.1007/s00521-026-12401-8

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Abstract

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With the expansion of clean energy and momentum toward the United Nations' 2030 Sustainable Development Goals, wind power has emerged as a key element in the transition to renewable energy. However, wind energy’s unpredictable nature, driven by volatile weather conditions, makes accurate forecasting difficult. Methods like Long Short-Term Memory (LSTM) models have proven effective for temporal dependency tasks, while newer variants, such as Nested LSTM (NLSTM), offer enhanced capabilities for modeling complex time relationships. Temporal Convolutional Networks (TCNs), developed under the Convolutional Neural Network (CNN) architecture, have also gained attention as promising alternatives for sequence modeling. Therefore, this study proposes a hybrid model combining TCN and NLSTM to leverage the strengths of both architectures. It further integrates Variational Mode Decomposition (VMD) for handling nonstationary data, using both power and weather data for improved prediction performance. Additionally, a mutation-inspired modification of the Driving Training-Based Optimization (DTBO) algorithm dynamically tunes the model’s hyperparameters. The results demonstrate that the enhanced DTBO-aided TCN-NLSTM outperforms single-network architecture, achieving up to a 12.7% reduction in Root Mean Square Error (RMSE).

Keywords:

Wind power forecasting

Temporal convolutional network

Nested long-short term memory

Driving training-based optimization algorithm

Variational mode decomposition

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